As for optimal policy computation, a popular choice is to use
Partially Observable Markov Decision Processes (POMDPs), a sound and complete framework for modeling dynamical processes in uncertain environments [1]}.
Computing exact solutions for non-trivial POMDPs is computationally intractable [2]}, but in the recent years impressive progress was made in developing approximate solvers.
One of the most recent and efficient approximation methods for POMDP policies is Monte Carlo Tree Search (MCTS) [3]}, [4]}, [5]}, [6]}, a heuristic search algorithm that represents system states as nodes of a tree, and actions/observations as edges. The most influential solver for POMDPs which takes advantage of MCTS is Partially Observable Monte Carlo Planning (POMCP) [7]} which combines a Monte Carlo update of the agent's belief state with an MCTS-based policy.
The most recent extensions of POMCP include applications to multiagents problems [8]} and reward maximization while constraining the cost  [9]}.
Finally, [10]} uses the constraints on the state space to refine the belief space and increase policy performance. Here we build our AVS approach upon this method and propose a first methodology which integrates POMCP to AVS avoiding the training bottleneck of state-of-the-art AVS methods, allowing to move the agent and simultaneously learn the optimal policy.
